Shipped: AI Traffic Sign Safety — Generative AI & Computer Vision

An open-source generative AI and computer vision proof of concept exploring how deep learning can improve traffic sign safety assessment and support intelligent transportation infrastructure.

The project uses a Conditional Generative Adversarial Network (cGAN) to synthesize realistic variations of Stop, Yield, and Upward Arrow traffic signs based on retroreflectivity and contrast parameters. A separate Convolutional Neural Network (CNN) is trained on the synthetic images to classify traffic signs as SAFE or UNSAFE according to predefined MUTCD-based criteria.

Built with PyTorch and Google Colab, the framework integrates synthetic data generation, rule-based safety labeling, image processing, neural network training, and model evaluation. The CNN achieves 93.04% test accuracy and 94.67% validation accuracy on generated datasets.

The research demonstrates how generative AI can help address limited training data in transportation safety applications and explores potential uses in roadway infrastructure inspection and autonomous vehicle perception. This is an experimental research prototype, not a validated real-world traffic sign inspection system.